Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : May 10, 2017 - May 13, 2017
This paper presents a signal processing technique to recognize human mental states from steady state visual evoked potentials. In this method, multiple band pass filter is applied to the electroencephalographic signals in order to extract feature points. A neural network classifier is used to recognize the type of LED stimulus the user is gazing at in real-time. An experiment is conducted on three participants to evaluate the classification performance of the proposed method. In this experiment, each participants gazes at three types of LEDs flashing at different frequencies. Another experiment is also conducted to evaluate the efficiency of information transfer based on recognition time. Experiment results show that the proposed method can achieve a mean recognition accuracy of 80%. Furthermore, the average time taken to recognize the target LED stimulus is 4.74 seconds.
